本文選題：圖像編碼　切入點：屏幕內(nèi)容　出處：《中國圖象圖形學(xué)報》2017年07期

【摘要】：目的調(diào)色板編碼是屏幕內(nèi)容編碼的典型方法之一,其索引圖的編碼效率直接影響到調(diào)色板編碼算法的總體壓縮性能。但是,在處理物體前景和文字邊緣的過渡區(qū)或連接區(qū)索引時,現(xiàn)有索引圖預(yù)測編碼方法的效率仍有待改善。為此提出一種基于馬爾可夫模型的索引圖預(yù)測算法。方法隨機選取了2 000個局部預(yù)測失敗的索引值并將它們劃分為3類典型分布,發(fā)現(xiàn)前2類分布的索引值往往處于邊緣的灰度平滑過渡區(qū),相鄰索引值間呈現(xiàn)較為明顯的線性變化,進而提出采用1階2維馬爾可夫隨機過程來刻畫這種線性性。對于一個待預(yù)測索引值,首先利用1階2維馬爾可夫模型計算相鄰索引值的線性相關(guān)得到初始預(yù)測值,再利用顏色轉(zhuǎn)移概率最大化確定其最優(yōu)預(yù)測值。結(jié)果本文算法的預(yù)測準確率為97.53%,比多級預(yù)測算法(MSP)和基于局部方向相關(guān)性的預(yù)測算法分別平均提高了4.33%和2.10%,尤其適用于包含大量文字字符和幾何圖元的視頻序列的索引圖預(yù)測。并且,漸近時間復(fù)雜度與基于局部方向相關(guān)性的預(yù)測算法相當(dāng),明顯低于MSP。具體地,本文算法的實際運行時間比MSP算法節(jié)省了95.08%,比基于局部方向相關(guān)性的預(yù)測算法增加了35.46%。結(jié)論本文提出的基于馬爾可夫模型的索引圖預(yù)測算法通過發(fā)掘索引值在邊緣區(qū)域的線性相關(guān)性和特定的顏色轉(zhuǎn)移模式,提高了索引預(yù)測效率,并保持了較低的計算復(fù)雜度,可應(yīng)用在屏幕內(nèi)容文本/圖形塊的調(diào)色板編碼中。
[Abstract]:Objective the palette coding is one of the typical methods of screen content coding. The coding efficiency of the index graph directly affects the overall compression performance of the palette coding algorithm.However, the efficiency of the existing index prediction and coding methods is still improved when dealing with the index of the transition region or the link area of the foreground of the object and the edge of the text.Therefore, an index graph prediction algorithm based on Markov model is proposed.Methods two thousand index values of local prediction failure were randomly selected and divided into three types of typical distributions. It was found that the index values of the first two types of distributions were usually in the gray level smooth transition region of the edge.The linear variation between the adjacent index values is obvious, and the first-order 2-dimensional Markov stochastic process is proposed to characterize the linearity.For an index value to be predicted, the linear correlation of the adjacent index value is calculated by using the 1-order 2-D Markov model, and the optimal prediction value is determined by maximizing the color transfer probability.Results the prediction accuracy of this algorithm is 97.53, which is 4.33% and 2.10% higher than that of MSP-based multilevel prediction algorithm and local direction correlation algorithm, respectively. It is especially suitable for the prediction of video sequences with a large number of text characters and geometric elements.Moreover, the asymptotic time complexity is similar to the prediction algorithm based on local direction correlation, which is obviously lower than that of MSPs.Specifically, the actual running time of this algorithm is 95.08 less than that of MSP algorithm, and 35.46% more than the prediction algorithm based on local direction correlation.Conclusion the proposed index graph prediction algorithm based on Markov model can improve the efficiency of index prediction by exploring the linear correlation of index values in the edge region and the specific color transfer mode, and maintains a low computational complexity.Can be used in screen content text / graphics block palette coding.
【作者單位】： 遼寧師范大學(xué)計算機與信息技術(shù)學(xué)院;大連理工大學(xué)計算機科學(xué)與技術(shù)學(xué)院;
【基金】：國家自然科學(xué)基金項目(61402214,41271422) 高等學(xué)校博士學(xué)科點專項科研基金項目(20132136110002) 遼寧省教育廳科學(xué)研究一般基金項目(L2015285,L201683681) 大連市青年科技之星項目支持計劃基金項目(2015R069)~~
【分類號】：O211.62;TN919.81
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本文編號：1709576